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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Seismic Tomographic Modeling of the Crust and Upper Mantle beneath Israel and the Middle East: Improved Resolution through Optimized Model Parameterization

Accurate regional seismic travel-time (RSTT) predictions rely on regional phases (e.g., Pg, Lg, Pn, Sn ) to account for 3D effects in the crust and upper mantle that are not captured by 1D models traditionally used for real-time location. The RSTT prediction model accounts for regional-scale crust and upper mantle structure globally by incorporating regional seismic phases into its travel-time calculations. Previous versions of the RSTT model have used a constant grid cell size of 1°. To improve the tomographic accuracy of recovering velocity structure at regional scales, we, in this study, perform data-driven grid refinement on the RSTT model down to a 0.125° grid (~14 km) in pursuit of two main goals: (1) to test the limits of RSTT capability and accuracy of determined velocity structure through variable grid refinement and (2) to image smaller structures in Israel and the Middle East and illuminate upper mantle dynamics operating in this complex tectonic area. We investigate the effects of model parameterization as grid cell size decreases and the trade-offs between recovered velocity structures. Our final dataset includes 4751 events and 499 stations that recorded 79,344 Pn and 7489 Pg . The variable grid refinement method allows recovery of finer-scale velocity structures and reduces travel-time residuals in areas with the highest data coverage. At smaller grid cell sizes, longer paths need to be upweighted to stabilize the inversion. Results illuminate tectonic features undefined in coarser grid-size models; in particular, we observe mantle perturbations related to the subduction zone around the Cyprian arc and crustal anomalies near the Dead Sea fault and throughout the Anatolian plate.

58 GEOSCIENCES↗
DOE OSTI2013

A linear programming model for optimizing HDR brachytherapy dose distributions with respect to mean dose in the DVH-tail

Purpose: Recent research has shown that the optimization model hitherto used in high-dose-rate (HDR) brachytherapy corresponds weakly to the dosimetric indices used to evaluate the quality of a dose distribution. Although alternative models that explicitly include such dosimetric indices have been presented, the inclusion of the dosimetric indices explicitly yields intractable models. The purpose of this paper is to develop a model for optimizing dosimetric indices that is easier to solve than those proposed earlier.Methods: In this paper, the authors present an alternative approach for optimizing dose distributions for HDR brachytherapy where dosimetric indices are taken into account through surrogates based on the conditional value-at-risk concept. This yields a linear optimization model that is easy to solve, and has the advantage that the constraints are easy to interpret and modify to obtain satisfactory dose distributions.Results: The authors show by experimental comparisons, carried out retrospectively for a set of prostate cancer patients, that their proposed model corresponds well with constraining dosimetric indices. All modifications of the parameters in the authors' model yield the expected result. The dose distributions generated are also comparable to those generated by the standard model with respect to the dosimetric indices that are used for evaluating quality.Conclusions: The authors' new model is a viable surrogate to optimizing dosimetric indices and quickly and easily yields high quality dose distributions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

R&D 100, 2016: Pyomo 4.0 – Python Optimization Modeling Objects

Pyomo provides a rich software environment for formulating and analyzing optimization applications. Pyomo supports the algebraic specification of complex sets of objectives and constraints, which enables optimization solvers to exploit problem structure to efficiently perform optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Development and Implementation of an Optimization Model for Hydropower and Total Dissolved Gas in the Mid-Columbia River System

Managing energy, water, and environmental priorities and constraints within a cascade hydropower system is a challenging multiobjective optimization effort that requires advanced modeling and forecasting tools. Within the mid-Columbia River system, there is currently a lack of specific solutions for predicting how coordinated operational decisions can mitigate the impacts of total dissolved gas (TDG) supersaturation while satisfying multiple additional policy and hydropower generation objectives. In this study, a reduced-order TDG uptake equation is developed that predicts tailrace TDG at seven hydropower facilities on the mid-Columbia River. The equation is incorporated into a general multiobjective river, reservoir, and hydropower optimization tool as a prioritized operating goal within a broader set of system-level objectives and constraints. A test case is presented to assess the response of TDG and hydropower generation when TDG supersaturation is optimized to remain under state water-quality standards. Satisfaction of TDG as an operating goal is highly dependent on whether constraints that limit TDG uptake are implemented at a higher priority than generation requests. According to the model, an opportunity exists to reduce TDG supersaturation and meet hydropower generation requirements by shifting spillway flows to different time periods. In conclusion, a coordinated effort between all project owners is required to implement systemwide optimized solutions that satisfy the operating policies of all stakeholders.

13 HYDRO ENERGY↗
DOE OSTI2014

WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules

Purpose: Concurrent chemo-radiation therapy (CCRT) has become a more common cancer treatment option with a better tumor control rate for several tumor sites, including head and neck and lung cancer. In this work, possible optimal chemotherapy schedules were investigated by implementing chemotherapy cell-kill into a tumor response model of RT. Methods: The chemotherapy effect has been added into a published model (Jeong et al., PMB (2013) 58:4897), in which the tumor response to RT can be simulated with the effects of hypoxia and proliferation. Based on the two-compartment pharmacokinetic model, the temporal concentration of chemotherapy agent was estimated. Log cell-kill was assumed and the cell-kill constant was estimated from the observed increase in local control due to concurrent chemotherapy. For a simplified two cycle CCRT regime, several different starting times and intervals were simulated with conventional RT regime (2Gy/fx, 5fx/wk). The effectiveness of CCRT was evaluated in terms of reduction in radiation dose required for 50% of control to find the optimal chemotherapy schedule. Results: Assuming the typical slope of dose response curve (γ50=2), the observed 10% increase in local control rate was evaluated to be equivalent to an extra RT dose of about 4 Gy, from which the cell-kill rate of chemotherapy was derived to be about 0.35. Best response was obtained when chemotherapy was started at about 3 weeks after RT began. As the interval between two cycles decreases, the efficacy of chemotherapy increases with broader range of optimal starting times. Conclusion: The effect of chemotherapy has been implemented into the resource-conservation tumor response model to investigate CCRT. The results suggest that the concurrent chemotherapy might be more effective when delayed for about 3 weeks, due to lower tumor burden and a larger fraction of proliferating cells after reoxygenation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Data-Driven Generic Turbines for Distributed Wind Modeling, Optimization, and Economic Studies

As distributed energy resources (DER) become less expensive and more popular, utilities, project developers, and customers have an increasing need to model the performance of existing and proposed DER systems. Distributed wind has been shown to have widespread economic potential but is often represented by a simplified model in - or excluded from - DER modeling tools and studies. There is often no economic imperative to extend models and studies to give full consideration to distributed wind. We present a set of data-driven generic turbines derived from 16 years of annual distributed wind market survey data. The proposed methodology can be used to derive generic turbines from separate or updated data sets. Finally, a mixed-integer linear programming approach to optimal distributed wind project sizing is used to demonstrate the generic turbine models. Combined, these models and methods can reduce barriers to considering distributed wind in modeling tools and studies.

Reiman, Andrew P.↗
DOE OSTI2022

Optimization Models For Drone Deployment

Model that supports drone deployment. Analysis on speed, package weight, energy consumption, # of drones, and battery replacements. This software developed tools for drone deployment optimization for direct delivery by introducing a new model that presents new insights addressing real-life issues. Specifically, this developed a new mixed-integer programming model with both time windows and battery replacements.

Roni, MohammadS↗
DOE OSTI2019

A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation

We report over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-driven framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Integrated Technology for Cost-Effective CO2 Capture and Formic Acid Production: Modeling, Optimization, and Economic Analysis

A novel reactive technology is being investigated that electrochemically converts CO2 into valuable chemicals, particularly formic acid. This work focuses on identifying the optimal design and operation of an integrated membrane-based CO2 capture unit with the electrochemical conversion process. In this setup, the CO2 in the flue gas permeates through a CO2-selective membrane and enters an electrolyzer to produce formic acid, creating an integrated reaction module. To refine the chemical product, gas products from the electrolyzer are directed to a pressure swing adsorption unit, while the liquid product undergoes refinement to achieve commercial-grade formic acid using reactive distillation. A membrane CO2 capture model and an electrochemical conversion model have been developed using the IDAES Integrated Platform (Institute for the Design of Advanced Energy System), facilitating rigorous flowsheet modeling and process design and optimization.

Wang, Maojian↗
DOE OSTI2026

Numerical Modeling & Optimization of the iProTech Pitching Inertial Pump (PIP) Wave Energy Converter (WEC) (CRADA Final Report)

This project represents a continuation of the collaboration between iProTech and NLR to simulate, optimize and design the iProTech Pitching Inertial Pump (PIP) device. The objectives of this TEAMER project are twofold: 1. Refining the physical characteristics of the existing iProTech PIP WEC-Sim model to enhance the model’s fidelity and include controllable components. Key model enhancements target the inclusion of Coulomb friction, the introduction of a controllable bypass valve, and the replacement of traditional check valves with advanced motorized ones. 2. Exploring traditional and advanced control algorithms. From traditional methods like latching control to cutting-edge reinforcement learning (RL) algorithms, the goal is to ensure the PIP device's adaptability and optimal performance across a range of ocean conditions. NLR is tasked with augmenting the WEC-Sim model and implementing the control algorithms, culminating in performance comparison analyses. iProTech will update their existing 3D models, advise on model improvements, and determine crucial system metrics. WEC-Sim, developed in MATLAB/SIMULINK with Simscape Multibody, is the main piece of software that will be used in this project. Coupled with the MATLAB RL Toolbox, it offers a robust platform for in-depth simulation and optimization of the iProTech PIP device. Building on previous work to explore the PIP design space and optimize its geometry, mass distribution, center of gravity and other key parameters, this project aims to refine iProTech’s existing numerical models and develop effective control algorithms that can seamlessly integrate into their future hardware testing campaigns.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Modeling, Optimization, and Design of Experiments of a Rotary Packed Bed Contactor for NGCC–Based CO2 Capture Using Solid Sorbents

This presentation will be given at the 2024 AICHE annual meeting on October 30th. This presentation focuses on modeling a rotary packed bed contactor for CO2 capture. The RPB is an alternative contactor to fixed beds and optimization is performed to minimize the energy requirement. A design of experiments case study of the RPB is also performed.

Hughes, Ryan↗
DOE OSTI2022

BISON fuel performance modeling optimization for experiment X447 and X447A using axial swelling and cladding strain measurements

With the recent need to qualify new reactor designs such as the Versatile Test Reactor (VTR), fuel performance calculations need to be performed to determine safety criteria of the proposed designs. In order to validate the fuel performance results obtained by a fuel performance code, BISON, for new reactor designs, legacy fuel from EBR-II and FFTF MFF with Post -Irradiation Examination (PIE) data need to be used as validation cases to benchmark models. Here in this work, BISON has been paired with the Fuels Irradiation & Physics Database (FIPD) and IFR Materials Information System (IMIS) to supply PIE data for comparison with simulations of EBR-II experiments X447/X447A. X447/X447A were assessed by implementing models for Fuel Cladding Chemical Interaction (FCCI) within BISON and optimizing the friction coefficient between the fuel surface and the cladding, the anisotropic swelling factor, and the HT9 first thermal creep scalar (which scales the first term in the HT9 creep equation) to best match the PIE axial fuel swelling height and cladding profilometry for all pins in X447/X447A. The optimal values were found using a generic algorithm developed to select different values for the three parameters until end criteria was met and error couldn’t be reduced further. The BISON-simulated cladding profilometry was evaluated using Standard Error of the Estimate (SEE) to account for the profile shape of the cladding profilometry. Optimal values for the friction coefficient, anisotropic fuel swelling factor, and HT9 first thermal creep scalar were found to best fit the BISON simulation results to the PIE measurements found in IMIS and FIPD. Improvements to current models are suggested to account for the underprediction of fuel swelling at low burnups and the overprediction of fuel swelling at higher burnups observed for the axial fuel swelling height. Although two pins in EBR-II X447/X447A (DP70 and DP75) were known to fail due to FCCI, none of the pins simulated in BISON reached a cumulative damage fraction (CDF) above 0.008 with FCCI correlations coupled in the BISON simulations. The error estimate generated for all pins in X447/X447A using optimal values was 209 µm, which is deemed acceptable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A multi-objective optimization model for cropland design considering profit, biodiversity, and ecosystem services

More sustainable agricultural methods are needed to alleviate the decreases in biodiversity and ecosystem services that have occurred because of industrial agriculture. One such method is the inclusion of alternative crops into croplands that can support biodiversity, reduce erosion and chemical runoff, and sequester carbon in the soil. However, the question of where such crops should be planted to balance competing economic and environmental objectives remains open. To this end, we develop a mixed-integer quadratically constrained program to optimize the layout of a cropland considering economic, biodiversity, greenhouse gas emissions, and water quality objectives. We include spatially varying fertilization as a decision variable in addition to crop establishment location. We further include the effect of core area and edges between different crops on biodiversity. To demonstrate the applicability of the model, we apply it to an example field, showing how the optimal cropland design changes as a decision-maker prioritizes different objectives and as edges have different impacts on biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Neuromuscular Junction Model Optimized for Electrical Platforms

Neuromuscular junctions (NMJs), specialized synapses between motor neurons and muscle fibers, are essential for muscle activity. A simple and reproducible cell-based in vitro NMJ platform is needed to test the impact of chemicals on the neuron-muscle communication. Our platform utilizes genetically modified neurons and muscle cells, optimized culture conditions, and commercially available multielectrode array system for recording action potentials. Neuronal cells (NSC34) were optogenetically modified with channelrhodopsin chimera to allow for simultaneous, light-mediated, millisecond-precise activation of neuronal population. This signal is propagated through functional synapses to the muscle fibers. Muscle cells (C2C12) were modified by incorporating gap junction protein (Connexin-43) to improve intracellular communication without affecting muscle differentiation. This communication between muscle fibers resulted in better signal propagation and signal strength. Optimized culture medium facilitated the growth and differentiation of both cell types together. Our system was validated using vecuronium, a muscle relaxant, which abolished the muscle response. This in vitro model provides a unique tool for establishing a NMJ platform that is easy to record and analyze. Potential applications include nondestructive long-term screening of drugs affecting the NMJ.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Circuit model optimization of a nano split ring resonator dimer antenna operating in infrared spectral range

Metamaterials are comprised of metallic structures with a strong response to incident electromagnetic radiation, like, for example, split ring resonators. The interaction of resonator ensembles with electromagnetic waves can be simulated with finite difference or finite elements algorithms, however, above a certain ensemble size simulations become inadmissibly time or memory consuming. Alternatively a circuit description of metamaterials, a well developed modelling tool at radio and microwave frequencies, allows to significantly increase the simulated ensemble size. This approach can be extended to the IR spectral range with an appropriate set of circuit element parameters accounting for physical effects such as electron inertia and finite conductivity. The model is verified by comparing the coupling coefficients with the ones obtained from the full wave numerical simulations, and used to optimize the nano-antenna design with improved radiation characteristics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

SDOM (Storage Deployment Optimization Model) [SWR-21-73]

SDOM is designed to accurately represent the operation of energy storage across different timescales, including long-duration and seasonal applications, and the spatiotemporal diversity and complementarity among VRE sources. SDOM uses an hourly temporal resolution, a fine spatial resolution for VRE sources, and a 1-year optimization window. SDOM assumes that all builds of VRE are accompanied by sufficient additional transmission capacity to allow full utilization of these additional resources. Nuclear, hydropower, and other renewable generation (e.g., biomass and geothermal energy sources) are fixed based on operational data (time series) for a given year; thus, SDOM minimizes total system cost using conventional generators as balancing units and using VRE and storage technologies to achieve a user-defined carbon-free or renewable energy target. The total system cost includes capital costs, fixed operation-and-maintenance (FO&M) costs, variable operation-and-maintenance (VO&M) costs, and fuel cost for power generation and storage technologies.

Guerra Fernandez, Omar Jose↗
DOE OSTI2019

Performance optimization, modeling and analysis of sparse matrix-matrix products on multi-core and many-core processors

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. In this work, we firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. We build the performance model for hash-table and heap-based algorithms, which supports the recipe. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix. Finally, we integrate our implementations into a large-scale protein clustering code named HipMCL, accelerating its SpGEMM kernel by up to 10X and achieving an overall performance boost for the whole HipMCL application by 2.6X.

97 MATHEMATICS AND COMPUTING↗